Improvement Algorithm for Limited Space Scheduling by Zouein, Pierrette

“…The model characterizes resource space requirements over time and establishes a time-space relationship for each activity in the schedule, based on alternative resource levels. …”
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Evolutionary algorithm for protein structure prediction by Mansour, Nashat

“…In this paper, we present an improved genetic algorithm (GA) for predicting 3D structures of proteins based on the hydrophobic polar model. …”
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Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast d... by Tarik Elhadd (5480393)

“…<h3>Objective</h3><p dir="ltr">To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan.…”

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. </p><h3>Conclusions </h3><p dir="ltr">This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. …”

Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. …”

Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis by Sami Elzeiny (16891521)

“…This work leverages the output of wearable technology to provide automatic stress and stressor identification model. In particular, this study proposes a novel algorithm that first detects instances of stress and then classifies the stressor type using photoplethysmography (PPG) data from wearable smartwatches. …”

Robust Kalman filter and smoother for errors-in-variables model with observation outliers based on Least-Trimmed-Squares by ALMutawa, Jaafar

“…Keywords: Errors-in-variables model, Least-Trimmed- Squares, Kalman filter and smoother, outliers, random search algorithm, subsampling method.…”
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Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models by Theng, Lau Wei

“…Pre-processing on the dataset is required to standardize the dataset by resizing the image into 224 * 224 pixels, convert into jpg format and augmentation. Based on the accuracy result from the model, the best model for the salak classification is ResNet50 which gave an accuracy of 84% followed by VGG16 that gave an accuracy of 77% and CNN which gave 31%.…”
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Stochastic Geometry-Based Model for Dynamic Allocation of Metering Equipment in Spatio-Temporal Expanding Power Grids by Atat, Rachad

“…Using the developed model, a multi-year algorithm for the allocation of metering equipment is proposed based on finite horizon dynamic programming, given budgetary and technical constraints on system observability. …”
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On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach by Md. Sihab Uddin (17542488)

“…The design is carried out based on the selection of the optimal model parameters using a search optimization algorithm called GridSearchCV. …”

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications by Abu Zitar, Raed

“…In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. …”
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A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns by Md Ferdous Wahid (13485799)

“…The first model (M1) determines the oil-water FP, whereas the second model (M2) predicts the oil-water PG. 1637 experimental data points for the oil-water flow in both horizontal and inclined pipes are used to develop the models. …”

A kernelization algorithm for d-Hitting Set by Abu-Khzam, Faisal N.

“…For 3-Hitting Set, an arbitrary instance is reduced into an equivalent one that contains at most 5k2+k elements. This kernelization is an improvement over previously known methods that guarantee cubic-order kernels. …”
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Optimal Dispatch of Mobile Energy Storage Unit to Support EV Charging Stations by Elmeligy, Mohamed Mostafa Abdelazim

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An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models by K. Mukilan (22224793)

“…To address these challenges, a claim management system is developed based on the Improved Monarch Butterfly Optimization Algorithm (IMBOA) and the principles of EPC. …”

The Effects of Data Mining on Small Businesses in Dubai by AlMutawa, Rasha

“…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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Weather Sensitivity Of Physically Based Model Of Residential Air-Conditioners For Direct Load Control: A Case Study by El-Ferik, S

“…Results reveals that the physically based model succeed to capture the effect of the outdoor weather conditions. …”
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Weather Sensitivity Of Physically Based Model Of Residential Air-Conditioners For Direct Load Control: A Case Study by El-Ferik, S

“…Results reveals that the physically based model succeed to capture the effect of the outdoor weather conditions. …”
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Modeling and thermoeconomic analysis of new polygeneration system based on geothermal energy with sea water desalination and hydrogen production by Wulaer Shaersaikai (21436652)

“…With strong R-squared values and high predictive accuracy, the Random Forest machine learning model predicts exergy efficiency, freshwater production, unit specific product cost (USPC), net present value (NPV), and environmental impact. …”

A machine learning model for early detection of diabetic foot using thermogram images by Amith Khandakar (14151981)

“…A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. …”